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Multi-objective Evolutionary Algorithms for Solving the Optimization Problem of the Surface Mounting

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Advanced Mechanical Science and Technology for the Industrial Revolution 4.0 (FZU 2016)

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Abstract

The multi-objective particle swarm optimization (MOPSO) is tested by the four ZDT problems. According to the simulation, the MOPSO can locate the global Pareto front (PF) on any instance. Moreover, a three-objective mathematical model is established to verify the ability of MOPSO when solving the practical engineering problems. The simulations show that, compared with the NSGA-II, the MOPSO is able to obtain better optimization results in a short period.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, 350116, People’s Republic of China

    Xuewei Ju, Guangyu Zhu & Shixiang Chen

Authors
  1. Xuewei Ju
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  2. Guangyu Zhu
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  3. Shixiang Chen
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Corresponding author

Correspondence to Guangyu Zhu .

Editor information

Editors and Affiliations

  1. Fuzhou University , Fuzhou, Fujian, China

    Ligang Yao

  2. Fuzhou University , Fuzhou, Fujian, China

    Shuncong Zhong

  3. Osaka Prefecture University , Sakai, Osaka, Japan

    Hisao Kikuta

  4. National Taiwan Ocean University , Taipei, Taiwan

    Jih-Gau Juang

  5. Fuzhou University , Fuzhou, China

    Masakazu Anpo

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© 2018 Springer Nature Singapore Pte Ltd.

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Ju, X., Zhu, G., Chen, S. (2018). Multi-objective Evolutionary Algorithms for Solving the Optimization Problem of the Surface Mounting. In: Yao, L., Zhong, S., Kikuta, H., Juang, JG., Anpo, M. (eds) Advanced Mechanical Science and Technology for the Industrial Revolution 4.0. FZU 2016. Springer, Singapore. https://doi.org/10.1007/978-981-10-4109-9_30

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  • DOI: https://doi.org/10.1007/978-981-10-4109-9_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4108-2

  • Online ISBN: 978-981-10-4109-9

  • eBook Packages: EngineeringEngineering (R0)

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